The majority of theoretical contributions in MARL have focused on highly structured classes of stochastic games, such as two-player zero-sum games [1]}, [2]} and \(N\) -player stochastic teams and their generalizations [3]}, [4]}. In much of the existing literature on MARL, a great deal of information is assumed to be available to the agents while they learn. These assumptions, such as full state observability ([1]}–[4]}) or action-sharing among all agents (e.g. [7]}, [8]}, [9]}), are appropriate in some settings but are unrealistic in many large-scale, decentralized systems modelled by MFGs.
